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检索条件"任意字段=3rd International Conference on Computing and Data Science, CONF-CDS 2021"

共 907 条记录，以下是101-110 订阅

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart data Integration and Processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

International Workshop on Artificial Intelligence for IT Ope...

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international Workshop on Artificial Intelligence for IT Operations, AIOps 2021, 3rd Workshop on Smart data Integration and Processing, STRAPS 2021, international Workshop on AI-enabled Process Automation, AI-PA 2021 and Scientific Satellite Events held in conjunction with 19th international conference on Service-Oriented computing, ICSOC 2021

ISBN: (纸本)9783031141348

The proceedings contain 32 papers presendted at a virtual meeting. The special focus in this conference is on Artificial Intelligence for IT Operations. The topics include: A Taxonomy of Anomalies in Log data;little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detection;MMRCA: MultiModal Root Cause Analysis;IAD: Indirect Anomalous VMMs Detection in the Cloud-Based Environment;comparing Graph data science Libraries for Querying and Analysing datasets: Towards data science Queries on Graphs;gamification for Healthier Lifestyle – User Retention;streaming and Visualising Neuronal Signals for Understanding Pain;graph Analytics Workflows Enactment on Just in Time data Centres Position Paper;say No2Ads: Automatic Advertisement and Music Filtering from Broadcast News Content;data Centred Intelligent Geosciences: Research Agenda and Opportunities Position Paper;Design Patterns in an ERP Environment;towards a Semantics-Based Search Engine for Smart Contract Information;a Blockchain-Based Autonomous System for Primary Financial Market;multi-agent Reinforcement Learning for Task Allocation in Cooperative Edge Cloud computing;occupancy Estimation from Thermal Images;constraint-Aware Trajectory for Drone Delivery Services;seamless Synchronization for Collaborative Web Services;Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform;AI-Enabled Processes: The Age of Artificial Intelligence and Big data;hybrid Recommendation of Movies Based on Deep Content Features;continuously Testing Distributed IoT Systems: An Overview of the State of the Art;towards Scalable Blockchains Using Service-Oriented Architectures;snapshot of Research Issues in Service Robots;automatic Image Annotation Using Quantization Reweighting Function and Graph Neural Networks;interactive Process Improvement Using Simulation of Enriched Process Trees;a Reproducible Approach for Mining Business Activities from Emails for Process Analytics;human-in

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Analysis of Deep Learning Object Detection Methods 3

Analysis of Deep Learning Object Detection Methods

引用

3rd international conference on Machine Learning and Computer Application, ICMLCA 2022

作者： Ai, Xiaofei He, Qiwen Zhang, Pan Guangxi University of Science and Technology Guangxi Province Liuzhou City545000 China Hechi University Guangxi Province Hechi546300 China

ISBN: (纸本)9781510664814

Deep learning-based target detection technology shows excellent performance in healthcare, industry, and transportation. The learning ability of convoluted neural networks (CNN) derives from a combination of feature extraction layers that make the most of a large amount of data. However, they usually require adequate computing and memory resources. Traditional target detection techniques have many limitations, but by using the features of deep learning self-learning, target detection techniques can reduce the complexity of artificial feature extraction. In this paper, the single-stage and two-stage target detection algorithms are analyzed from the angle of detector algorithm, and their respective characteristics are summarized. The most commonly used datasets of target detection are summarized and the evaluation indexes of target detection algorithm are analyzed. Finally, the prospect of the future is given. © 2023 SPIE.

关键词： Object detection

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Energy Efficiency in IoT-Based Smart Healthcare 3rd

Energy Efficiency in IoT-Based Smart Healthcare

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3rd international conference on computing, Communications, and Cyber-Security, IC4S 2021

作者： Sangra, Pallavi Rana, Bharti Singh, Yashwant Department of Computer Science and Information Technology Central University of Jammu Jammu and Kashmir Samba181143 India

ISBN: (纸本)9789811911415

IoT-based smart healthcare is a new technological shift toward efficient, convenient, cheaper, and faster medical services using artificial learning, big data analytics, sensor technologies, and cloud computing. Smart healthcare reduces the time and cost to avail the services region-wise rather than to get the clinical services at distant locations. Along with this, smart healthcare poses many challenging issues. One of the greatest challenges in smart healthcare is to accomplish the energy-efficient services as smart nodes are energy constrained. Therefore, this study addresses the energy consumption challenges in smart healthcare sector. Then, we focus on energy preserving mechanisms to reduce the energy consumption. The various energy-conserving mechanisms such as intelligent techniques, duty cycling techniques, collision resolution techniques, and edge techniques in context to smart healthcare have been discussed for reducing energy consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy consumption Energy efficiency Internet of things Smart healthcare

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Research on Cloud computing Security Technology of Power System 1

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3rd international conference on Big data and Security, ICBDS 2021

作者： Xiong, Hongzhang Cheng, Jie Wang, YanJin Yang, Xiaokun Wang, Kuo Beijing100045 China

ISBN: (数字)9789811908521

ISBN: (纸本)9789811908514

The integrated function of cloud computing technology can effectively guarantee the security of power system data, and lay a solid foundation for the healthy and stable development of power system. At present, the application of cloud computing security technology in the power system is still in the primary stage, and there is a large space for improvement in both the application depth and the strategy level. On account of this, this paper first analyzes the overall security requirements of power system and the concept and connotation of cloud computing security technology in power system. Then it proposes the security structure target of power system. Finally, this paper analyses and studies the power system architecture of cloud computing security and applied technologies of cloud computing security. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Sensitive data

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Evaluation and Application of Cloud computing in Mental Health Service Platform 3

Evaluation and Application of Cloud Computing in Mental Heal...

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3rd international conference on Machine Learning and Big data Analytics for IoT Security and Privacy, SPIoT2023

作者： Han, Xue Bai, Lu Han, Bing Management School Harbin University of Commerce Heilongjiang Harbin150028 China School of Accounting Harbin University of Commerce Heilongjiang Harbin150028 China School of Food and Engineering Harbin University of Commerce Heilongjiang Harbin150028 China

With the continuous development of the times, internet technology has gradually matured, and internet medicine has gradually entered the field of mental health. The mental health service platform can help people identify their psychological problems and provide treatment, but there are still shortcomings in current mental health service platforms, so new technologies need to be added for improvement. This article focuses on the research and application of cloud computing in mental health service platforms, aiming to improve the quality of mental health service platforms through cloud computing. Through experiments, this paper tests that after using cloud computing technology, the accuracy of the mental health service platform for users' psychological test is 78% at the highest and 70% at the lowest, while the accuracy of the mental health service platform for users' psychological test before using cloud computing is 60% at the highest and 50% at the lowest. From this experimental data, it can be seen that the use of cloud computing in mental health service platforms is very effective. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cloud computing

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Energy Saving Techniques for Cloud data Centres: An Empirical Research Analysis 3rd

Energy Saving Techniques for Cloud Data Centres: An Empirica...

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3rd international conference on Machine Learning, Image Processing, Network Security and data sciences, MIND 2021

作者： Shehloo, Arif Ahmad Butt, Muheet Ahmed Zaman, Majid Research Scholar Mewar University Rajasthan India P.G. Deparment of Computer Science University of Kashmir Srinagar India Directorate of IT & Support System University of Kashmir Srinagar India

ISBN: (纸本)9789811958670

Platforms for cloud computing make it possible for end users to get access to computing resources online. The emergence of cloud computing has led to an increase in the use of Apache Hadoop in cloud data centres. MapReduce and the open-source framework Hadoop have generated great interest among both researchers and corporates. Optimising Hadoop’s energy consumption has been identified as a critical issue, with numerous studies undertaken on the topic. FIFO, capacity, and fair scheduling algorithms are the most commonly used scheduling algorithms in Hadoop cloud data centres. This paper examines the nature and non-nature-oriented energy-efficient scheduling techniques that are frequently used in cloud data centres. The studies that have been explored are classified into node management, virtualisation and consolidation, task scheduling, data placement, and infrastructure change categories. A brief discussion of the rationale for each category is included, along with its strengths and weaknesses. Additionally, for computation and I/O-intensive workloads, the paper examines the CPU energy consumption of three commonly used cloud workload scheduling algorithms: FIFO, fair, and capacity. Our goal is to see if the energy usage among the three schedulers differs significantly between two types of workloads and different job sizes. Finally, the cloud computing research community has been provided with a futuristic perspective in the area, based on current trends in energy-efficient algorithms. The findings and directions presented in this study may be helpful to researchers interested in optimising cloud data centre energy consumption. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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PM2.5 Density Prediction Based on a Two-Stage Rolling Forecast Model Using LightGBM 3rd

PM2.5 Density Prediction Based on a Two-Stage Rolling Foreca...

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3rd international conference on computing and data science, conf-cds 2021

作者： Liu, Zihao Beijing Haidian International School No. 368-2 Hanhe Road Haidian Beijing China

ISBN: (纸本)9789811688843

At present, air pollution is a primary issue of the world. Particularly, PM2.5 pollution can cause severe impact on economy and human health, so developing an accurate PM2.5 prediction model becomes a hot topic. Up to now, researchers had developed PM2.5 forecasting methods based on decision tree models, RNN models, and hybrid models. Previous works also discovered plentiful features, such as seasonal data and weather forecasting data that help increase the accuracy of PM2.5 prediction. To improve the model accuracy, we developed a LightGBM-based PM2.5 prediction model that has two innovations: 1) our model studies how special events (e.g., diplomatic visits, sport events, and government meetings) influence PM2.5 variation. 2) our model adopts the strategy of two-stage rolling forecasting so that it can achieve high accuracy without relying on weather forecasting data. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Weather forecasting

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Research on water extraction method from remote sensing images of lakes in cold and arid regions based on deep learning 3

Research on water extraction method from remote sensing imag...

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3rd international conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Wang, Xin Fu, Xueliang Hu, Hua Li, Honghui College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research Application of Agriculture and Animal Husbandry Hohhot China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock mo

关键词： Lakes

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Programmable NIC based Task Offload Framework 3

Programmable NIC based Task Offload Framework

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3rd international conference on Electronics and Communication;Network and Computer Technology (ECNCT)

作者： Huang, Jinfeng Yang, Xiangrui Natl Univ Def Technol Coll Comp Changsha Peoples R China

ISBN: (数字)9781510652118

ISBN: (纸本)9781510652118;9781510652101

Using programmable NIC to offload network processing tasks such as checksum computation, virtual switching has become a common case in data centers. Such offloading mechanisms is able to help saving CPU cycles on servers, thus allowing data centers providing more computing resources to users for commercial benefits. However, recent research indicates that even with the offloading features above, data center servers still suffer from a great portion of CPU time being devoted to computing tasks that are unrelated to tenants. Such tasks include but not limited to data encryption and decryption, compression and decompression, etc. These tasks are common in modern data centers and cost a high amount of computational resources. Thus, in this paper, we propose a framework to offload those tasks onto programmable NIC, which will avoid high CPU usage on data center servers. On the one hand, the proposed framework is able to support multiple computing tasks running on the same programmable NIC at the same time. On the other hand, with a novel indexing mechanism, users are able to determine the order of different tasks at compile-time. Last but not least, the feasibility and advantages have been validated via our early stage experiments.

关键词： programmable NIC data center offload CPU time

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Research on the visual identification and classification-based perception method of trash bins for automated garbage trucks 3

Research on the visual identification and classification-bas...

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3rd international conference on Artificial Intelligence, Information Processing and Cloud computing, AIIPCC 2022

作者： Leng, Xinlun Sun, Bohua Zhang, Yufei Li, Yaxin Lv, Jiliang Liu, Qiang State Key Laboratory of Automotive Simulation and Control Jilin University Changchun130025 China Automotive Data of China Co. Ltd. CATARC Tianjin300000 China Jilin Engineering Normal University Changchun130025 China

ISBN: (纸本)9783800759330

With the development of autonomous driving technology, there is a trend toward automation in special vehicles including garbage trucks. Due to its specialized use, how to identify and classify trash bins in time can become the key problem of automated garbage trucks. In this research, we proposed a trash bin perception method for garbage trucks, which integrates a trash bin identification method based on conventional neural network (CNN) and a trash bin classification method based on color recognition. According to the relative position of target trash bin, the autonomous driving system can stop the garbage truck at the right position and grasp the target trash bin in time. Test shows that the method designed in this research can recognition the target trash bin rapidly, and realize the automatic operation cooperated with the autonomous driving system. © VDE VERLAG GMBH.

关键词： Autonomous vehicles

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